Following [1]}, [2]}, [3]}, [4]}, We use VGG-16 as our backbone. The training set is a subset of the COCO dataset [5]} (9213 images) and saliency dataset DUTS [6]}, as suggested by [1]}. In training iteration, 8 (\(N=8\) ) images from a sub-group of COCO dataset and 8 (\(K=8\) ) images from DUTS are simultaneously fed into the network for jointly optimizing. The images are all resized to \(256 \times 256\)  for training and testing, and the output co-saliency maps are resized to the original size for evaluation. The network is trained over 100 epochs in total with the Adam optimizer. The initial learning rate is set to \(1e-4\) , \(\beta _1=0.9\)  and \(\beta _2=0.99\) .
